If the noise in the output traffic states is non-Gaussian and follows complex distributions, the filtering methods could fail.

GANs have been used to learn complex distributions of stochasticity residing in random fields from ST data [1]}.

Key issues with GANs include mode collapse and training instability [2]}.
One solution is to regularize the training of GANs by incorporating physics into the structure of generators (and potentially discriminators), where unlabeled data are generated from generators that are guided by physics-based models, so that the search space of parameters is narrowed down.

